{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import codecs\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'batch_size':32,\n",
    "    'lr' : 0.01,\n",
    "    'max_sent_len': 20,\n",
    "    'epochs': 500,\n",
    "    'drops' : [0.1]\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_data(data_path):\n",
    "    \"\"\"\n",
    "    意图识别抽取出label\n",
    "    槽位识别与填充作为命名实体识别问题，对每一个字进行实体标注, ate_time', 'B-target', 'I-date_time', 'I-date_time', 'I-operation', 'I-date_time', 'I-date_time']\n",
    "[ ]:\n",
    "￼\n",
    "​B E I O S#         print(txt_seqs[index])\n",
    "    \"\"\"\n",
    "    with codecs.open(data_path,\"r\",encoding=\"utf-8\") as fp:\n",
    "        data = json.load(fp)\n",
    "    texts = [example['text'].replace(\" \",\"\") for example in data]\n",
    "    intent_labels = [example['intent'] for example in data]\n",
    "    \n",
    "    slots_ners = []\n",
    "    count = 0\n",
    "    for example in data:\n",
    "        if 'entities' in example.keys():\n",
    "            text = example['text']\n",
    "            ner = ['O'] * len(text)\n",
    "            slots = example['entities']\n",
    "            for key,val in slots.items():\n",
    "                start_idx = text.find(val)\n",
    "                end_idx = start_idx + len(val) -1\n",
    "                if len(val) == 1:\n",
    "                    ner[start_idx] = 'S-' + key\n",
    "                else:\n",
    "                    ner[start_idx] = 'B-' + key\n",
    "                    ner[end_idx] = 'E-'+ key\n",
    "                    for idx in range(start_idx+1, end_idx):\n",
    "                        ner[idx] = 'I-' + key\n",
    "        else:\n",
    "            text = example['text']\n",
    "            ner = ['O'] * len(text)\n",
    "        slots_ners.append(ner)\n",
    "    print('texts len: ', len(texts))\n",
    "    print('intent_lables len: ',len(intent_labels))\n",
    "    print('slots_ners len: ', len(slots_ners))\n",
    "    return texts, intent_labels, slots_ners        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "texts len:  2506\n",
      "intent_lables len:  2506\n",
      "slots_ners len:  2506\n"
     ]
    }
   ],
   "source": [
    "data_path =\"../dataset/data_v2.json\"\n",
    "max_sent_len = params[\"max_sent_len\"]\n",
    "texts, intent, slots_ners = extract_data(data_path)\n",
    "l = len(texts) // params['batch_size']\n",
    "texts = texts[:l*params['batch_size']]\n",
    "intent_labels =  intent[:l*params['batch_size']]\n",
    "slots_ners = slots_ners[:l*params['batch_size']]\n",
    "\n",
    "train_text = [d for i , d in enumerate(texts) if i % 10 != 0]\n",
    "train_l = len(train_text) // params['batch_size']\n",
    "train_text = train_text[:train_l*params['batch_size']]\n",
    "\n",
    "valid_text = [d for i , d in enumerate(texts) if i % 10 == 0]\n",
    "valid_l = len(valid_text) // params['batch_size']\n",
    "valid_text = valid_text[:valid_l*params['batch_size']]\n",
    "\n",
    "train_intent = [d for i , d in enumerate(intent_labels) if i % 10 != 0]\n",
    "train_intent = train_intent[:train_l*params['batch_size']]\n",
    "valid_intent = [d for i , d in enumerate(intent_labels) if i % 10 == 0]\n",
    "valid_intent = valid_intent[:valid_l*params['batch_size']]\n",
    "\n",
    "train_ner = [d for i , d in enumerate(slots_ners) if i % 10 != 0]\n",
    "train_ner = train_ner[:train_l*params['batch_size']]\n",
    "valid_ner = [d for i , d in enumerate(slots_ners) if i % 10 == 0]\n",
    "valid_ner =valid_ner[:valid_l*params['batch_size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('/home/ai/hgm/Smart_Home/ner_model/char_conv.json', mode='r', encoding='utf-8') as f:\n",
    "    dicts = json.load(f)\n",
    "    \n",
    "char2id = dicts['char2id']\n",
    "id2char = dicts['id2char']\n",
    "intent2id = dicts['intent2id']\n",
    "id2intent = dicts['id2intent']\n",
    "slot2id = dicts['slot2id']\n",
    "id2slot = dicts['id2slot']\n",
    "\n",
    "params['intent_num'] = len(intent2id)\n",
    "params['slot_num'] = len(slot2id)\n",
    "params['id2intent'] = id2intent\n",
    "params['id2slot'] = id2slot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans2labelid(vocab, labels, max_sent_len):\n",
    "    labels = [vocab[label] for label in labels]\n",
    "    if len(labels) < max_sent_len:\n",
    "        labels += [0] * (max_sent_len - len(labels))\n",
    "    else:\n",
    "        labels = labels[:max_sent_len]\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(txt_seqs, intent_labels, slot_ners,char2id,intent2id,slot2id,max_sent_len):\n",
    "    dataset_text_labels = []\n",
    "    dataset_intent_labels = []\n",
    "    dataset_ner_labels = []\n",
    "    \n",
    "    for index in range(len(txt_seqs)):\n",
    "        dataset_text_labels.append(trans2labelid(char2id,txt_seqs[index],max_sent_len))\n",
    "        dataset_intent_labels.append([intent2id[intent_labels[index]]])\n",
    "        dataset_ner_labels.append(trans2labelid(slot2id,slot_ners[index],max_sent_len))\n",
    "    dataset_text_labels = np.array(dataset_text_labels)\n",
    "    dataset_intent_labels = np.array(dataset_intent_labels)\n",
    "    dataset_ner_labels = np.array(dataset_ner_labels)\n",
    "    \n",
    "    return dataset_text_labels, dataset_intent_labels, dataset_ner_labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tarin_seq, train_intent, train_ner =  read_data(texts, intent_labels, slots_ners,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_seq, train_intent, train_ner =  read_data(train_text, train_intent, train_ner,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_seq, valid_intent, valid_ner =  read_data(valid_text, valid_intent, valid_ner,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Dataset(txt_seqs, dataset_intent_labels, dataset_ner_labels):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(({\n",
    "    \"Input\" : txt_seqs\n",
    "    },\n",
    "    {\n",
    "        \"pre_intent\":dataset_intent_labels,\n",
    "        \n",
    "        \"pre_ner\":dataset_ner_labels\n",
    "    }))\n",
    "    l = len(txt_seqs)\n",
    "    dataset = dataset.shuffle(l)\n",
    "    dataset = dataset.batch(params['batch_size'])\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = Dataset(train_seq, train_intent, train_ner)\n",
    "valid_dataset = Dataset(valid_seq, valid_intent, valid_ner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "params['intent_num'] = len(intent2id)\n",
    "params['slot_num'] = len(slot2id)\n",
    "params['id2intent'] = id2intent\n",
    "params['id2slot'] = id2slot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input (InputLayer)              [(None, 20)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 20, 64)       32000       Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional (Bidirectional)   (None, 20, 128)      49920       embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization (LayerNorma (None, 20, 128)      256         bidirectional[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d (Globa (None, 128)          0           layer_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_1 (LayerNor (None, 20, 128)      256         bidirectional[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "pre_intent (Dense)              (None, 50)           6450        global_average_pooling1d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "pre_ner (Dense)                 (None, 20, 36)       4644        layer_normalization_1[0][0]      \n",
      "==================================================================================================\n",
      "Total params: 93,526\n",
      "Trainable params: 93,526\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "text_inputs = tf.keras.layers.Input(shape=(20,),name='Input')\n",
    "embed = tf.keras.layers.Embedding(500,64)(text_inputs)\n",
    "bilstm = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64,return_sequences=True))(embed)\n",
    "x_in = tf.keras.layers.LayerNormalization()(bilstm)\n",
    "x_conv = tf.keras.layers.GlobalAveragePooling1D()(x_in)\n",
    "pre_intent = tf.keras.layers.Dense(params['intent_num'],\\\n",
    "            activation='softmax',name = 'pre_intent')(x_conv)\n",
    "x_ner  = tf.keras.layers.LayerNormalization()(bilstm)\n",
    "pre_slot = tf.keras.layers.Dense(params['slot_num'],activation='softmax',name = 'pre_ner')(x_ner)\n",
    "model = tf.keras.Model(text_inputs,[pre_intent,pre_slot])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_loss(y_true,y_pred):\n",
    "    return tf.keras.losses.sparse_categorical_crossentropy(y_true,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = {'pre_intent':'sparse_categorical_crossentropy','pre_ner':'sparse_categorical_crossentropy'}\n",
    "metrics = { 'pre_intent': ['accuracy'],'pre_ner': ['accuracy']}\n",
    "optimizer = tf.keras.optimizers.Adam(params['lr'])\n",
    "model.compile(optimizer, loss=losses, metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = '../ner_model_weight/model_conv.h5'\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(file_path,\n",
    "                                                        save_weights_only=False, save_best_only=True)\n",
    "learning_rate_reduction = tf.keras.callbacks.ReduceLROnPlateau(patience=50, factor=0.5)\n",
    "callbacks_list = [checkpoint,learning_rate_reduction]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "70/70 [==============================] - 1s 12ms/step - loss: 1.8404 - pre_intent_loss: 1.3397 - pre_ner_loss: 0.5007 - pre_intent_accuracy: 0.7063 - pre_ner_accuracy: 0.8838 - val_loss: 0.7210 - val_pre_intent_loss: 0.4604 - val_pre_ner_loss: 0.2606 - val_pre_intent_accuracy: 0.8705 - val_pre_ner_accuracy: 0.9411\n",
      "Epoch 2/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.3801 - pre_intent_loss: 0.1821 - pre_ner_loss: 0.1980 - pre_intent_accuracy: 0.9500 - pre_ner_accuracy: 0.9512 - val_loss: 0.4013 - val_pre_intent_loss: 0.2046 - val_pre_ner_loss: 0.1967 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9509\n",
      "Epoch 3/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.2336 - pre_intent_loss: 0.0821 - pre_ner_loss: 0.1515 - pre_intent_accuracy: 0.9777 - pre_ner_accuracy: 0.9597 - val_loss: 0.3177 - val_pre_intent_loss: 0.1312 - val_pre_ner_loss: 0.1865 - val_pre_intent_accuracy: 0.9643 - val_pre_ner_accuracy: 0.9547\n",
      "Epoch 4/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.1749 - pre_intent_loss: 0.0519 - pre_ner_loss: 0.1229 - pre_intent_accuracy: 0.9857 - pre_ner_accuracy: 0.9660 - val_loss: 0.3349 - val_pre_intent_loss: 0.1933 - val_pre_ner_loss: 0.1417 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9645\n",
      "Epoch 5/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.1397 - pre_intent_loss: 0.0322 - pre_ner_loss: 0.1075 - pre_intent_accuracy: 0.9893 - pre_ner_accuracy: 0.9695 - val_loss: 0.3014 - val_pre_intent_loss: 0.1714 - val_pre_ner_loss: 0.1300 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9681\n",
      "Epoch 6/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.1247 - pre_intent_loss: 0.0338 - pre_ner_loss: 0.0909 - pre_intent_accuracy: 0.9911 - pre_ner_accuracy: 0.9735 - val_loss: 0.2773 - val_pre_intent_loss: 0.1498 - val_pre_ner_loss: 0.1274 - val_pre_intent_accuracy: 0.9643 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 7/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.1014 - pre_intent_loss: 0.0211 - pre_ner_loss: 0.0803 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9751 - val_loss: 0.2902 - val_pre_intent_loss: 0.1696 - val_pre_ner_loss: 0.1207 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9676\n",
      "Epoch 8/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0925 - pre_intent_loss: 0.0200 - pre_ner_loss: 0.0725 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9778 - val_loss: 0.2885 - val_pre_intent_loss: 0.1642 - val_pre_ner_loss: 0.1243 - val_pre_intent_accuracy: 0.9688 - val_pre_ner_accuracy: 0.9665\n",
      "Epoch 9/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0858 - pre_intent_loss: 0.0231 - pre_ner_loss: 0.0627 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9804 - val_loss: 0.2758 - val_pre_intent_loss: 0.1543 - val_pre_ner_loss: 0.1215 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 10/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0921 - pre_intent_loss: 0.0315 - pre_ner_loss: 0.0606 - pre_intent_accuracy: 0.9902 - pre_ner_accuracy: 0.9802 - val_loss: 0.3761 - val_pre_intent_loss: 0.2532 - val_pre_ner_loss: 0.1229 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 11/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.1026 - pre_intent_loss: 0.0379 - pre_ner_loss: 0.0647 - pre_intent_accuracy: 0.9888 - pre_ner_accuracy: 0.9796 - val_loss: 0.3473 - val_pre_intent_loss: 0.2107 - val_pre_ner_loss: 0.1366 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9699\n",
      "Epoch 12/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0919 - pre_intent_loss: 0.0294 - pre_ner_loss: 0.0625 - pre_intent_accuracy: 0.9902 - pre_ner_accuracy: 0.9806 - val_loss: 0.3586 - val_pre_intent_loss: 0.2399 - val_pre_ner_loss: 0.1187 - val_pre_intent_accuracy: 0.9598 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 13/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0682 - pre_intent_loss: 0.0170 - pre_ner_loss: 0.0512 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9839 - val_loss: 0.3274 - val_pre_intent_loss: 0.2160 - val_pre_ner_loss: 0.1114 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 14/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0645 - pre_intent_loss: 0.0180 - pre_ner_loss: 0.0465 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9847 - val_loss: 0.3722 - val_pre_intent_loss: 0.2601 - val_pre_ner_loss: 0.1120 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 15/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0579 - pre_intent_loss: 0.0144 - pre_ner_loss: 0.0435 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9857 - val_loss: 0.3224 - val_pre_intent_loss: 0.2129 - val_pre_ner_loss: 0.1095 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9766\n",
      "Epoch 16/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0574 - pre_intent_loss: 0.0190 - pre_ner_loss: 0.0384 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9869 - val_loss: 0.3458 - val_pre_intent_loss: 0.2130 - val_pre_ner_loss: 0.1328 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 17/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0558 - pre_intent_loss: 0.0163 - pre_ner_loss: 0.0394 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9869 - val_loss: 0.3497 - val_pre_intent_loss: 0.2295 - val_pre_ner_loss: 0.1202 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 18/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0512 - pre_intent_loss: 0.0102 - pre_ner_loss: 0.0410 - pre_intent_accuracy: 0.9955 - pre_ner_accuracy: 0.9858 - val_loss: 0.3418 - val_pre_intent_loss: 0.2192 - val_pre_ner_loss: 0.1226 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9705\n",
      "Epoch 19/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0454 - pre_intent_loss: 0.0109 - pre_ner_loss: 0.0345 - pre_intent_accuracy: 0.9969 - pre_ner_accuracy: 0.9877 - val_loss: 0.3805 - val_pre_intent_loss: 0.2646 - val_pre_ner_loss: 0.1159 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 20/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0460 - pre_intent_loss: 0.0128 - pre_ner_loss: 0.0331 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9880 - val_loss: 0.3656 - val_pre_intent_loss: 0.2554 - val_pre_ner_loss: 0.1102 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9772\n",
      "Epoch 21/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0445 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0328 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9884 - val_loss: 0.3664 - val_pre_intent_loss: 0.2496 - val_pre_ner_loss: 0.1168 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 22/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0410 - pre_intent_loss: 0.0116 - pre_ner_loss: 0.0294 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9898 - val_loss: 0.3581 - val_pre_intent_loss: 0.2412 - val_pre_ner_loss: 0.1169 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9763\n",
      "Epoch 23/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0444 - pre_intent_loss: 0.0115 - pre_ner_loss: 0.0329 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9885 - val_loss: 0.3844 - val_pre_intent_loss: 0.2613 - val_pre_ner_loss: 0.1231 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 24/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0535 - pre_intent_loss: 0.0193 - pre_ner_loss: 0.0343 - pre_intent_accuracy: 0.9906 - pre_ner_accuracy: 0.9873 - val_loss: 0.4215 - val_pre_intent_loss: 0.2653 - val_pre_ner_loss: 0.1562 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9650\n",
      "Epoch 25/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.2290 - pre_intent_loss: 0.1175 - pre_ner_loss: 0.1114 - pre_intent_accuracy: 0.9643 - pre_ner_accuracy: 0.9667 - val_loss: 0.5531 - val_pre_intent_loss: 0.3587 - val_pre_ner_loss: 0.1944 - val_pre_intent_accuracy: 0.9152 - val_pre_ner_accuracy: 0.9565\n",
      "Epoch 26/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.2273 - pre_intent_loss: 0.1058 - pre_ner_loss: 0.1215 - pre_intent_accuracy: 0.9728 - pre_ner_accuracy: 0.9678 - val_loss: 0.4602 - val_pre_intent_loss: 0.3068 - val_pre_ner_loss: 0.1534 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9629\n",
      "Epoch 27/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.1246 - pre_intent_loss: 0.0475 - pre_ner_loss: 0.0771 - pre_intent_accuracy: 0.9848 - pre_ner_accuracy: 0.9768 - val_loss: 0.5354 - val_pre_intent_loss: 0.3950 - val_pre_ner_loss: 0.1403 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9692\n",
      "Epoch 28/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0992 - pre_intent_loss: 0.0375 - pre_ner_loss: 0.0617 - pre_intent_accuracy: 0.9893 - pre_ner_accuracy: 0.9804 - val_loss: 0.4281 - val_pre_intent_loss: 0.3029 - val_pre_ner_loss: 0.1251 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 29/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0660 - pre_intent_loss: 0.0198 - pre_ner_loss: 0.0462 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9848 - val_loss: 0.4214 - val_pre_intent_loss: 0.2982 - val_pre_ner_loss: 0.1232 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 30/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0610 - pre_intent_loss: 0.0225 - pre_ner_loss: 0.0384 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9869 - val_loss: 0.4323 - val_pre_intent_loss: 0.3097 - val_pre_ner_loss: 0.1226 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 31/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0507 - pre_intent_loss: 0.0151 - pre_ner_loss: 0.0356 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9871 - val_loss: 0.4284 - val_pre_intent_loss: 0.2992 - val_pre_ner_loss: 0.1292 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 32/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0453 - pre_intent_loss: 0.0136 - pre_ner_loss: 0.0317 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9890 - val_loss: 0.4511 - val_pre_intent_loss: 0.3161 - val_pre_ner_loss: 0.1350 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9699\n",
      "Epoch 33/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0423 - pre_intent_loss: 0.0127 - pre_ner_loss: 0.0296 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9901 - val_loss: 0.4495 - val_pre_intent_loss: 0.3043 - val_pre_ner_loss: 0.1452 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9705\n",
      "Epoch 34/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0361 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0244 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9911 - val_loss: 0.4231 - val_pre_intent_loss: 0.2977 - val_pre_ner_loss: 0.1254 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 35/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0347 - pre_intent_loss: 0.0113 - pre_ner_loss: 0.0234 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9911 - val_loss: 0.4778 - val_pre_intent_loss: 0.3413 - val_pre_ner_loss: 0.1365 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 36/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0314 - pre_intent_loss: 0.0112 - pre_ner_loss: 0.0201 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9924 - val_loss: 0.4529 - val_pre_intent_loss: 0.3278 - val_pre_ner_loss: 0.1251 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 37/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0330 - pre_intent_loss: 0.0110 - pre_ner_loss: 0.0220 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9917 - val_loss: 0.4667 - val_pre_intent_loss: 0.3250 - val_pre_ner_loss: 0.1417 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 38/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0288 - pre_intent_loss: 0.0098 - pre_ner_loss: 0.0190 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9929 - val_loss: 0.4615 - val_pre_intent_loss: 0.3166 - val_pre_ner_loss: 0.1449 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 39/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0346 - pre_intent_loss: 0.0124 - pre_ner_loss: 0.0222 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9920 - val_loss: 0.4714 - val_pre_intent_loss: 0.3239 - val_pre_ner_loss: 0.1476 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 40/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0344 - pre_intent_loss: 0.0116 - pre_ner_loss: 0.0228 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9916 - val_loss: 0.4645 - val_pre_intent_loss: 0.3188 - val_pre_ner_loss: 0.1457 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 41/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0355 - pre_intent_loss: 0.0098 - pre_ner_loss: 0.0257 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9908 - val_loss: 0.4558 - val_pre_intent_loss: 0.3151 - val_pre_ner_loss: 0.1407 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 42/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0331 - pre_intent_loss: 0.0109 - pre_ner_loss: 0.0222 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9919 - val_loss: 0.4638 - val_pre_intent_loss: 0.3118 - val_pre_ner_loss: 0.1520 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 43/500\n",
      "70/70 [==============================] - 0s 6ms/step - loss: 0.0374 - pre_intent_loss: 0.0116 - pre_ner_loss: 0.0258 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9908 - val_loss: 0.4935 - val_pre_intent_loss: 0.3389 - val_pre_ner_loss: 0.1546 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 44/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0378 - pre_intent_loss: 0.0104 - pre_ner_loss: 0.0274 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9906 - val_loss: 0.4897 - val_pre_intent_loss: 0.3239 - val_pre_ner_loss: 0.1658 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 45/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0436 - pre_intent_loss: 0.0107 - pre_ner_loss: 0.0330 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9895 - val_loss: 0.5477 - val_pre_intent_loss: 0.3847 - val_pre_ner_loss: 0.1629 - val_pre_intent_accuracy: 0.9196 - val_pre_ner_accuracy: 0.9676\n",
      "Epoch 46/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0455 - pre_intent_loss: 0.0132 - pre_ner_loss: 0.0323 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9889 - val_loss: 0.4627 - val_pre_intent_loss: 0.3124 - val_pre_ner_loss: 0.1503 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 47/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0599 - pre_intent_loss: 0.0134 - pre_ner_loss: 0.0465 - pre_intent_accuracy: 0.9955 - pre_ner_accuracy: 0.9855 - val_loss: 0.5028 - val_pre_intent_loss: 0.3414 - val_pre_ner_loss: 0.1614 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9667\n",
      "Epoch 48/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0665 - pre_intent_loss: 0.0189 - pre_ner_loss: 0.0476 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9844 - val_loss: 0.4867 - val_pre_intent_loss: 0.3347 - val_pre_ner_loss: 0.1520 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9692\n",
      "Epoch 49/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0597 - pre_intent_loss: 0.0166 - pre_ner_loss: 0.0431 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9857 - val_loss: 0.4876 - val_pre_intent_loss: 0.3469 - val_pre_ner_loss: 0.1407 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9694\n",
      "Epoch 50/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0584 - pre_intent_loss: 0.0176 - pre_ner_loss: 0.0408 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9861 - val_loss: 0.4579 - val_pre_intent_loss: 0.3143 - val_pre_ner_loss: 0.1436 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 51/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0382 - pre_intent_loss: 0.0102 - pre_ner_loss: 0.0281 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9900 - val_loss: 0.4623 - val_pre_intent_loss: 0.3306 - val_pre_ner_loss: 0.1318 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 52/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0357 - pre_intent_loss: 0.0123 - pre_ner_loss: 0.0234 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9916 - val_loss: 0.4671 - val_pre_intent_loss: 0.3200 - val_pre_ner_loss: 0.1471 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 53/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0338 - pre_intent_loss: 0.0113 - pre_ner_loss: 0.0225 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9925 - val_loss: 0.4714 - val_pre_intent_loss: 0.3244 - val_pre_ner_loss: 0.1471 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9746\n",
      "Epoch 54/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0310 - pre_intent_loss: 0.0107 - pre_ner_loss: 0.0203 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9927 - val_loss: 0.4475 - val_pre_intent_loss: 0.3028 - val_pre_ner_loss: 0.1447 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 55/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0298 - pre_intent_loss: 0.0113 - pre_ner_loss: 0.0186 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9931 - val_loss: 0.4286 - val_pre_intent_loss: 0.2761 - val_pre_ner_loss: 0.1525 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 56/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0286 - pre_intent_loss: 0.0125 - pre_ner_loss: 0.0160 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9940 - val_loss: 0.4860 - val_pre_intent_loss: 0.3231 - val_pre_ner_loss: 0.1629 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 57/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0306 - pre_intent_loss: 0.0113 - pre_ner_loss: 0.0193 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9931 - val_loss: 0.4464 - val_pre_intent_loss: 0.2841 - val_pre_ner_loss: 0.1623 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9694\n",
      "Epoch 58/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0288 - pre_intent_loss: 0.0085 - pre_ner_loss: 0.0203 - pre_intent_accuracy: 0.9964 - pre_ner_accuracy: 0.9929 - val_loss: 0.4249 - val_pre_intent_loss: 0.2712 - val_pre_ner_loss: 0.1537 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 59/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0356 - pre_intent_loss: 0.0154 - pre_ner_loss: 0.0202 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9934 - val_loss: 0.4385 - val_pre_intent_loss: 0.2760 - val_pre_ner_loss: 0.1625 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 60/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0231 - pre_intent_loss: 0.0095 - pre_ner_loss: 0.0136 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9948 - val_loss: 0.4513 - val_pre_intent_loss: 0.2913 - val_pre_ner_loss: 0.1601 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 61/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0180 - pre_intent_loss: 0.0089 - pre_ner_loss: 0.0091 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9962 - val_loss: 0.4412 - val_pre_intent_loss: 0.2825 - val_pre_ner_loss: 0.1587 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 62/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0165 - pre_intent_loss: 0.0083 - pre_ner_loss: 0.0082 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9965 - val_loss: 0.4499 - val_pre_intent_loss: 0.2862 - val_pre_ner_loss: 0.1638 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 63/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0166 - pre_intent_loss: 0.0087 - pre_ner_loss: 0.0079 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.4493 - val_pre_intent_loss: 0.2834 - val_pre_ner_loss: 0.1659 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 64/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0162 - pre_intent_loss: 0.0083 - pre_ner_loss: 0.0079 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.4478 - val_pre_intent_loss: 0.2841 - val_pre_ner_loss: 0.1637 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 65/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0155 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0075 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9962 - val_loss: 0.4518 - val_pre_intent_loss: 0.2859 - val_pre_ner_loss: 0.1658 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9752\n",
      "Epoch 66/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0151 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0072 - pre_intent_accuracy: 0.9955 - pre_ner_accuracy: 0.9967 - val_loss: 0.4528 - val_pre_intent_loss: 0.2832 - val_pre_ner_loss: 0.1696 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 67/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0157 - pre_intent_loss: 0.0086 - pre_ner_loss: 0.0071 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.4541 - val_pre_intent_loss: 0.2867 - val_pre_ner_loss: 0.1674 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 68/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0157 - pre_intent_loss: 0.0086 - pre_ner_loss: 0.0072 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.4595 - val_pre_intent_loss: 0.2935 - val_pre_ner_loss: 0.1661 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 69/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0157 - pre_intent_loss: 0.0082 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9967 - val_loss: 0.4626 - val_pre_intent_loss: 0.2904 - val_pre_ner_loss: 0.1722 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 70/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0160 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.4633 - val_pre_intent_loss: 0.2935 - val_pre_ner_loss: 0.1698 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 71/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0150 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0066 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.4652 - val_pre_intent_loss: 0.2905 - val_pre_ner_loss: 0.1747 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 72/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0157 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0073 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.4529 - val_pre_intent_loss: 0.2833 - val_pre_ner_loss: 0.1696 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 73/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0159 - pre_intent_loss: 0.0082 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.4663 - val_pre_intent_loss: 0.2970 - val_pre_ner_loss: 0.1693 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 74/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0148 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0069 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.4711 - val_pre_intent_loss: 0.2952 - val_pre_ner_loss: 0.1758 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 75/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0144 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0067 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.4562 - val_pre_intent_loss: 0.2824 - val_pre_ner_loss: 0.1737 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9750\n",
      "Epoch 76/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0146 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0068 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.4669 - val_pre_intent_loss: 0.2959 - val_pre_ner_loss: 0.1711 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9743\n",
      "Epoch 77/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0154 - pre_intent_loss: 0.0081 - pre_ner_loss: 0.0073 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9964 - val_loss: 0.4598 - val_pre_intent_loss: 0.2867 - val_pre_ner_loss: 0.1731 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 78/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0163 - pre_intent_loss: 0.0085 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9965 - val_loss: 0.4872 - val_pre_intent_loss: 0.3089 - val_pre_ner_loss: 0.1783 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 79/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0173 - pre_intent_loss: 0.0097 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.4775 - val_pre_intent_loss: 0.2959 - val_pre_ner_loss: 0.1816 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 80/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0170 - pre_intent_loss: 0.0087 - pre_ner_loss: 0.0083 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.4600 - val_pre_intent_loss: 0.2891 - val_pre_ner_loss: 0.1709 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 81/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0154 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0070 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.4783 - val_pre_intent_loss: 0.2979 - val_pre_ner_loss: 0.1805 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 82/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0160 - pre_intent_loss: 0.0082 - pre_ner_loss: 0.0078 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.4729 - val_pre_intent_loss: 0.2998 - val_pre_ner_loss: 0.1731 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9748\n",
      "Epoch 83/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0155 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.4603 - val_pre_intent_loss: 0.2808 - val_pre_ner_loss: 0.1795 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 84/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0169 - pre_intent_loss: 0.0087 - pre_ner_loss: 0.0082 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.4616 - val_pre_intent_loss: 0.2885 - val_pre_ner_loss: 0.1731 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9743\n",
      "Epoch 85/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0156 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9965 - val_loss: 0.4692 - val_pre_intent_loss: 0.2955 - val_pre_ner_loss: 0.1737 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 86/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0150 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0072 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.4753 - val_pre_intent_loss: 0.2979 - val_pre_ner_loss: 0.1774 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 87/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0154 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9963 - val_loss: 0.4722 - val_pre_intent_loss: 0.2968 - val_pre_ner_loss: 0.1754 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 88/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0149 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0070 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.4694 - val_pre_intent_loss: 0.2985 - val_pre_ner_loss: 0.1709 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 89/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0154 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0070 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.4827 - val_pre_intent_loss: 0.3067 - val_pre_ner_loss: 0.1760 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9748\n",
      "Epoch 90/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0165 - pre_intent_loss: 0.0086 - pre_ner_loss: 0.0079 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.4713 - val_pre_intent_loss: 0.2937 - val_pre_ner_loss: 0.1776 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 91/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0171 - pre_intent_loss: 0.0087 - pre_ner_loss: 0.0084 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.4716 - val_pre_intent_loss: 0.2915 - val_pre_ner_loss: 0.1802 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 92/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0185 - pre_intent_loss: 0.0093 - pre_ner_loss: 0.0092 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9962 - val_loss: 0.4533 - val_pre_intent_loss: 0.2824 - val_pre_ner_loss: 0.1710 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9750\n",
      "Epoch 93/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0216 - pre_intent_loss: 0.0087 - pre_ner_loss: 0.0129 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9950 - val_loss: 0.4878 - val_pre_intent_loss: 0.3011 - val_pre_ner_loss: 0.1866 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9692\n",
      "Epoch 94/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0451 - pre_intent_loss: 0.0098 - pre_ner_loss: 0.0352 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9896 - val_loss: 0.4888 - val_pre_intent_loss: 0.2943 - val_pre_ner_loss: 0.1944 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9679\n",
      "Epoch 95/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0669 - pre_intent_loss: 0.0152 - pre_ner_loss: 0.0517 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9836 - val_loss: 0.4716 - val_pre_intent_loss: 0.2931 - val_pre_ner_loss: 0.1785 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9661\n",
      "Epoch 96/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0664 - pre_intent_loss: 0.0161 - pre_ner_loss: 0.0503 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9845 - val_loss: 0.5867 - val_pre_intent_loss: 0.4269 - val_pre_ner_loss: 0.1598 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9705\n",
      "Epoch 97/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0522 - pre_intent_loss: 0.0159 - pre_ner_loss: 0.0363 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9871 - val_loss: 0.5814 - val_pre_intent_loss: 0.4235 - val_pre_ner_loss: 0.1579 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 98/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0412 - pre_intent_loss: 0.0160 - pre_ner_loss: 0.0252 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9909 - val_loss: 0.5065 - val_pre_intent_loss: 0.3524 - val_pre_ner_loss: 0.1541 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 99/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0282 - pre_intent_loss: 0.0106 - pre_ner_loss: 0.0176 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9935 - val_loss: 0.4602 - val_pre_intent_loss: 0.3175 - val_pre_ner_loss: 0.1427 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 100/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0230 - pre_intent_loss: 0.0101 - pre_ner_loss: 0.0129 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9954 - val_loss: 0.5052 - val_pre_intent_loss: 0.3496 - val_pre_ner_loss: 0.1556 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 101/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0191 - pre_intent_loss: 0.0083 - pre_ner_loss: 0.0108 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9959 - val_loss: 0.4987 - val_pre_intent_loss: 0.3396 - val_pre_ner_loss: 0.1591 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 102/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0187 - pre_intent_loss: 0.0097 - pre_ner_loss: 0.0090 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9962 - val_loss: 0.4879 - val_pre_intent_loss: 0.3316 - val_pre_ner_loss: 0.1563 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 103/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0186 - pre_intent_loss: 0.0101 - pre_ner_loss: 0.0085 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.4846 - val_pre_intent_loss: 0.3273 - val_pre_ner_loss: 0.1573 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 104/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0168 - pre_intent_loss: 0.0085 - pre_ner_loss: 0.0083 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9963 - val_loss: 0.4734 - val_pre_intent_loss: 0.3162 - val_pre_ner_loss: 0.1573 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 105/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0173 - pre_intent_loss: 0.0090 - pre_ner_loss: 0.0083 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.5005 - val_pre_intent_loss: 0.3370 - val_pre_ner_loss: 0.1635 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 106/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0160 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.4992 - val_pre_intent_loss: 0.3402 - val_pre_ner_loss: 0.1590 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 107/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0157 - pre_intent_loss: 0.0082 - pre_ner_loss: 0.0075 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5034 - val_pre_intent_loss: 0.3405 - val_pre_ner_loss: 0.1628 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 108/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0157 - pre_intent_loss: 0.0081 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.4916 - val_pre_intent_loss: 0.3289 - val_pre_ner_loss: 0.1626 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 109/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0161 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0082 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9964 - val_loss: 0.5081 - val_pre_intent_loss: 0.3423 - val_pre_ner_loss: 0.1658 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 110/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0143 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0064 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5041 - val_pre_intent_loss: 0.3402 - val_pre_ner_loss: 0.1639 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 111/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5056 - val_pre_intent_loss: 0.3401 - val_pre_ner_loss: 0.1655 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 112/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0131 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5085 - val_pre_intent_loss: 0.3429 - val_pre_ner_loss: 0.1656 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 113/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0131 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5057 - val_pre_intent_loss: 0.3398 - val_pre_ner_loss: 0.1659 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 114/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0132 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.5083 - val_pre_intent_loss: 0.3425 - val_pre_ner_loss: 0.1658 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 115/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0058 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9963 - val_loss: 0.5110 - val_pre_intent_loss: 0.3434 - val_pre_ner_loss: 0.1675 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 116/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5083 - val_pre_intent_loss: 0.3419 - val_pre_ner_loss: 0.1663 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 117/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0131 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9970 - val_loss: 0.5084 - val_pre_intent_loss: 0.3434 - val_pre_ner_loss: 0.1649 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 118/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0129 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5162 - val_pre_intent_loss: 0.3474 - val_pre_ner_loss: 0.1689 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 119/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9968 - val_loss: 0.5116 - val_pre_intent_loss: 0.3440 - val_pre_ner_loss: 0.1677 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 120/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0134 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.5118 - val_pre_intent_loss: 0.3436 - val_pre_ner_loss: 0.1682 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 121/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0129 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5121 - val_pre_intent_loss: 0.3436 - val_pre_ner_loss: 0.1686 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 122/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0138 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5076 - val_pre_intent_loss: 0.3403 - val_pre_ner_loss: 0.1673 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 123/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5168 - val_pre_intent_loss: 0.3464 - val_pre_ner_loss: 0.1704 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 124/500\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0126 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9928 - pre_ner_accuracy: 0.997 - 0s 4ms/step - loss: 0.0131 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5121 - val_pre_intent_loss: 0.3459 - val_pre_ner_loss: 0.1662 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 125/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0127 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5148 - val_pre_intent_loss: 0.3441 - val_pre_ner_loss: 0.1707 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 126/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0136 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0060 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5162 - val_pre_intent_loss: 0.3468 - val_pre_ner_loss: 0.1693 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 127/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0131 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.5151 - val_pre_intent_loss: 0.3445 - val_pre_ner_loss: 0.1705 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 128/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0130 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5190 - val_pre_intent_loss: 0.3506 - val_pre_ner_loss: 0.1684 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 129/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0130 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5139 - val_pre_intent_loss: 0.3432 - val_pre_ner_loss: 0.1708 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 130/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0134 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.5195 - val_pre_intent_loss: 0.3479 - val_pre_ner_loss: 0.1717 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 131/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5206 - val_pre_intent_loss: 0.3452 - val_pre_ner_loss: 0.1754 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 132/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0139 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9971 - val_loss: 0.5107 - val_pre_intent_loss: 0.3389 - val_pre_ner_loss: 0.1717 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 133/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0128 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9968 - val_loss: 0.5207 - val_pre_intent_loss: 0.3485 - val_pre_ner_loss: 0.1722 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 134/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0132 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5208 - val_pre_intent_loss: 0.3497 - val_pre_ner_loss: 0.1711 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 135/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0127 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0052 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5225 - val_pre_intent_loss: 0.3474 - val_pre_ner_loss: 0.1752 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 136/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0134 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9964 - val_loss: 0.5265 - val_pre_intent_loss: 0.3516 - val_pre_ner_loss: 0.1749 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 137/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0128 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5187 - val_pre_intent_loss: 0.3432 - val_pre_ner_loss: 0.1755 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 138/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0131 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.5260 - val_pre_intent_loss: 0.3479 - val_pre_ner_loss: 0.1780 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 139/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0140 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0061 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5262 - val_pre_intent_loss: 0.3530 - val_pre_ner_loss: 0.1732 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 140/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0133 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0058 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9963 - val_loss: 0.5228 - val_pre_intent_loss: 0.3494 - val_pre_ner_loss: 0.1733 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 141/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0130 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5285 - val_pre_intent_loss: 0.3540 - val_pre_ner_loss: 0.1745 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 142/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0136 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9972 - val_loss: 0.5433 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.1777 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 143/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0174 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0099 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9954 - val_loss: 0.5086 - val_pre_intent_loss: 0.3264 - val_pre_ner_loss: 0.1821 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 144/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0193 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0114 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9953 - val_loss: 0.5282 - val_pre_intent_loss: 0.3462 - val_pre_ner_loss: 0.1819 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9705\n",
      "Epoch 145/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0178 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0098 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9956 - val_loss: 0.5346 - val_pre_intent_loss: 0.3476 - val_pre_ner_loss: 0.1871 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 146/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0160 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0082 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9962 - val_loss: 0.5193 - val_pre_intent_loss: 0.3341 - val_pre_ner_loss: 0.1851 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 147/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0155 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9965 - val_loss: 0.5113 - val_pre_intent_loss: 0.3325 - val_pre_ner_loss: 0.1788 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 148/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0142 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0068 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.5146 - val_pre_intent_loss: 0.3331 - val_pre_ner_loss: 0.1815 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 149/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0145 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0064 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9962 - val_loss: 0.5147 - val_pre_intent_loss: 0.3319 - val_pre_ner_loss: 0.1827 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 150/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0134 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0058 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5215 - val_pre_intent_loss: 0.3400 - val_pre_ner_loss: 0.1815 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 151/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9967 - val_loss: 0.5250 - val_pre_intent_loss: 0.3410 - val_pre_ner_loss: 0.1840 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 152/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5206 - val_pre_intent_loss: 0.3399 - val_pre_ner_loss: 0.1807 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 153/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0130 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9964 - val_loss: 0.5148 - val_pre_intent_loss: 0.3331 - val_pre_ner_loss: 0.1817 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 154/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0129 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5259 - val_pre_intent_loss: 0.3468 - val_pre_ner_loss: 0.1792 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 155/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0140 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0067 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9964 - val_loss: 0.5270 - val_pre_intent_loss: 0.3439 - val_pre_ner_loss: 0.1831 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 156/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0135 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5158 - val_pre_intent_loss: 0.3389 - val_pre_ner_loss: 0.1769 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 157/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0132 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5191 - val_pre_intent_loss: 0.3455 - val_pre_ner_loss: 0.1736 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 158/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0127 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.5256 - val_pre_intent_loss: 0.3487 - val_pre_ner_loss: 0.1770 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 159/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0128 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5280 - val_pre_intent_loss: 0.3430 - val_pre_ner_loss: 0.1850 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 160/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0122 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0049 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.5293 - val_pre_intent_loss: 0.3474 - val_pre_ner_loss: 0.1820 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 161/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0116 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5301 - val_pre_intent_loss: 0.3497 - val_pre_ner_loss: 0.1804 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 162/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9970 - val_loss: 0.5306 - val_pre_intent_loss: 0.3500 - val_pre_ner_loss: 0.1806 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 163/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0048 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5302 - val_pre_intent_loss: 0.3504 - val_pre_ner_loss: 0.1798 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 164/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5319 - val_pre_intent_loss: 0.3510 - val_pre_ner_loss: 0.1809 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 165/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5346 - val_pre_intent_loss: 0.3539 - val_pre_ner_loss: 0.1808 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 166/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5392 - val_pre_intent_loss: 0.3590 - val_pre_ner_loss: 0.1803 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 167/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5368 - val_pre_intent_loss: 0.3572 - val_pre_ner_loss: 0.1796 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 168/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5346 - val_pre_intent_loss: 0.3548 - val_pre_ner_loss: 0.1798 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 169/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0118 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.5397 - val_pre_intent_loss: 0.3605 - val_pre_ner_loss: 0.1792 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 170/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9970 - val_loss: 0.5413 - val_pre_intent_loss: 0.3602 - val_pre_ner_loss: 0.1811 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 171/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5443 - val_pre_intent_loss: 0.3633 - val_pre_ner_loss: 0.1810 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 172/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.5375 - val_pre_intent_loss: 0.3562 - val_pre_ner_loss: 0.1813 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 173/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9972 - val_loss: 0.5409 - val_pre_intent_loss: 0.3587 - val_pre_ner_loss: 0.1822 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 174/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5411 - val_pre_intent_loss: 0.3613 - val_pre_ner_loss: 0.1798 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 175/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5443 - val_pre_intent_loss: 0.3629 - val_pre_ner_loss: 0.1814 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 176/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5478 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.1823 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 177/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5497 - val_pre_intent_loss: 0.3671 - val_pre_ner_loss: 0.1826 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 178/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0121 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0048 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5492 - val_pre_intent_loss: 0.3672 - val_pre_ner_loss: 0.1821 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 179/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0120 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0048 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5495 - val_pre_intent_loss: 0.3669 - val_pre_ner_loss: 0.1826 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 180/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5497 - val_pre_intent_loss: 0.3684 - val_pre_ner_loss: 0.1813 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 181/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0121 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0049 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.5490 - val_pre_intent_loss: 0.3686 - val_pre_ner_loss: 0.1804 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 182/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5515 - val_pre_intent_loss: 0.3707 - val_pre_ner_loss: 0.1808 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 183/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0118 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5490 - val_pre_intent_loss: 0.3669 - val_pre_ner_loss: 0.1821 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 184/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5538 - val_pre_intent_loss: 0.3720 - val_pre_ner_loss: 0.1818 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 185/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5559 - val_pre_intent_loss: 0.3728 - val_pre_ner_loss: 0.1830 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 186/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0121 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0049 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5538 - val_pre_intent_loss: 0.3746 - val_pre_ner_loss: 0.1792 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 187/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0118 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5563 - val_pre_intent_loss: 0.3735 - val_pre_ner_loss: 0.1828 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 188/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0116 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9969 - val_loss: 0.5559 - val_pre_intent_loss: 0.3741 - val_pre_ner_loss: 0.1818 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 189/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5612 - val_pre_intent_loss: 0.3774 - val_pre_ner_loss: 0.1838 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 190/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0121 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0048 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5607 - val_pre_intent_loss: 0.3779 - val_pre_ner_loss: 0.1828 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 191/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0116 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9970 - val_loss: 0.5588 - val_pre_intent_loss: 0.3774 - val_pre_ner_loss: 0.1813 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 192/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.5588 - val_pre_intent_loss: 0.3770 - val_pre_ner_loss: 0.1818 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 193/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5666 - val_pre_intent_loss: 0.3822 - val_pre_ner_loss: 0.1844 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 194/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5627 - val_pre_intent_loss: 0.3802 - val_pre_ner_loss: 0.1825 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 195/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5633 - val_pre_intent_loss: 0.3792 - val_pre_ner_loss: 0.1841 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 196/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5646 - val_pre_intent_loss: 0.3818 - val_pre_ner_loss: 0.1829 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 197/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.5630 - val_pre_intent_loss: 0.3805 - val_pre_ner_loss: 0.1825 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 198/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9964 - val_loss: 0.5658 - val_pre_intent_loss: 0.3823 - val_pre_ner_loss: 0.1835 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 199/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0120 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5672 - val_pre_intent_loss: 0.3820 - val_pre_ner_loss: 0.1852 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 200/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5665 - val_pre_intent_loss: 0.3823 - val_pre_ner_loss: 0.1842 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 201/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5710 - val_pre_intent_loss: 0.3887 - val_pre_ner_loss: 0.1824 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 202/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0122 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0049 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5734 - val_pre_intent_loss: 0.3896 - val_pre_ner_loss: 0.1839 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 203/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0120 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9965 - val_loss: 0.5724 - val_pre_intent_loss: 0.3892 - val_pre_ner_loss: 0.1832 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 204/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0048 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5669 - val_pre_intent_loss: 0.3841 - val_pre_ner_loss: 0.1828 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 205/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5688 - val_pre_intent_loss: 0.3863 - val_pre_ner_loss: 0.1825 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 206/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5735 - val_pre_intent_loss: 0.3888 - val_pre_ner_loss: 0.1847 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 207/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0116 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5798 - val_pre_intent_loss: 0.3945 - val_pre_ner_loss: 0.1852 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 208/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0120 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0048 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.5771 - val_pre_intent_loss: 0.3914 - val_pre_ner_loss: 0.1857 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 209/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0116 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5782 - val_pre_intent_loss: 0.3930 - val_pre_ner_loss: 0.1853 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 210/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5799 - val_pre_intent_loss: 0.3937 - val_pre_ner_loss: 0.1861 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 211/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5789 - val_pre_intent_loss: 0.3933 - val_pre_ner_loss: 0.1856 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 212/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5813 - val_pre_intent_loss: 0.3951 - val_pre_ner_loss: 0.1862 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 213/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.5823 - val_pre_intent_loss: 0.3963 - val_pre_ner_loss: 0.1860 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 214/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0113 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9963 - val_loss: 0.5788 - val_pre_intent_loss: 0.3928 - val_pre_ner_loss: 0.1860 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 215/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5805 - val_pre_intent_loss: 0.3950 - val_pre_ner_loss: 0.1855 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 216/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5807 - val_pre_intent_loss: 0.3944 - val_pre_ner_loss: 0.1863 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 217/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.5845 - val_pre_intent_loss: 0.3968 - val_pre_ner_loss: 0.1877 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 218/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5853 - val_pre_intent_loss: 0.3985 - val_pre_ner_loss: 0.1868 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 219/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0113 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5850 - val_pre_intent_loss: 0.3979 - val_pre_ner_loss: 0.1871 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 220/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5878 - val_pre_intent_loss: 0.3996 - val_pre_ner_loss: 0.1882 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 221/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5869 - val_pre_intent_loss: 0.3994 - val_pre_ner_loss: 0.1875 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 222/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5870 - val_pre_intent_loss: 0.3992 - val_pre_ner_loss: 0.1878 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 223/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9963 - val_loss: 0.5875 - val_pre_intent_loss: 0.3996 - val_pre_ner_loss: 0.1879 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 224/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.5866 - val_pre_intent_loss: 0.3980 - val_pre_ner_loss: 0.1886 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 225/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9971 - val_loss: 0.5901 - val_pre_intent_loss: 0.4010 - val_pre_ner_loss: 0.1891 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 226/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5884 - val_pre_intent_loss: 0.3994 - val_pre_ner_loss: 0.1890 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 227/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0113 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0043 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5916 - val_pre_intent_loss: 0.4025 - val_pre_ner_loss: 0.1891 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 228/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5903 - val_pre_intent_loss: 0.4009 - val_pre_ner_loss: 0.1894 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 229/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5934 - val_pre_intent_loss: 0.4036 - val_pre_ner_loss: 0.1898 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 230/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0113 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5903 - val_pre_intent_loss: 0.4005 - val_pre_ner_loss: 0.1899 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 231/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5916 - val_pre_intent_loss: 0.4018 - val_pre_ner_loss: 0.1898 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 232/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5928 - val_pre_intent_loss: 0.4032 - val_pre_ner_loss: 0.1896 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 233/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5944 - val_pre_intent_loss: 0.4038 - val_pre_ner_loss: 0.1906 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 234/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5967 - val_pre_intent_loss: 0.4060 - val_pre_ner_loss: 0.1907 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 235/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9969 - val_loss: 0.5957 - val_pre_intent_loss: 0.4050 - val_pre_ner_loss: 0.1906 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 236/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5953 - val_pre_intent_loss: 0.4039 - val_pre_ner_loss: 0.1914 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 237/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9968 - val_loss: 0.5979 - val_pre_intent_loss: 0.4066 - val_pre_ner_loss: 0.1914 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 238/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5997 - val_pre_intent_loss: 0.4079 - val_pre_ner_loss: 0.1918 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 239/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5994 - val_pre_intent_loss: 0.4086 - val_pre_ner_loss: 0.1907 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 240/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5993 - val_pre_intent_loss: 0.4078 - val_pre_ner_loss: 0.1915 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 241/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9966 - val_loss: 0.6008 - val_pre_intent_loss: 0.4087 - val_pre_ner_loss: 0.1921 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 242/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6022 - val_pre_intent_loss: 0.4099 - val_pre_ner_loss: 0.1923 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 243/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.6002 - val_pre_intent_loss: 0.4074 - val_pre_ner_loss: 0.1929 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 244/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9969 - val_loss: 0.6018 - val_pre_intent_loss: 0.4093 - val_pre_ner_loss: 0.1925 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 245/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.6004 - val_pre_intent_loss: 0.4081 - val_pre_ner_loss: 0.1922 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 246/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6004 - val_pre_intent_loss: 0.4084 - val_pre_ner_loss: 0.1920 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 247/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.6023 - val_pre_intent_loss: 0.4097 - val_pre_ner_loss: 0.1926 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 248/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9967 - val_loss: 0.6044 - val_pre_intent_loss: 0.4111 - val_pre_ner_loss: 0.1933 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 249/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6041 - val_pre_intent_loss: 0.4109 - val_pre_ner_loss: 0.1932 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 250/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0113 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.6060 - val_pre_intent_loss: 0.4125 - val_pre_ner_loss: 0.1935 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 251/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6052 - val_pre_intent_loss: 0.4119 - val_pre_ner_loss: 0.1934 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 252/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.6064 - val_pre_intent_loss: 0.4129 - val_pre_ner_loss: 0.1935 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 253/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6066 - val_pre_intent_loss: 0.4123 - val_pre_ner_loss: 0.1942 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 254/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.6058 - val_pre_intent_loss: 0.4127 - val_pre_ner_loss: 0.1931 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 255/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9965 - val_loss: 0.6070 - val_pre_intent_loss: 0.4132 - val_pre_ner_loss: 0.1938 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 256/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6089 - val_pre_intent_loss: 0.4152 - val_pre_ner_loss: 0.1937 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 257/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6096 - val_pre_intent_loss: 0.4149 - val_pre_ner_loss: 0.1948 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 258/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6104 - val_pre_intent_loss: 0.4160 - val_pre_ner_loss: 0.1943 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 259/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9965 - val_loss: 0.6065 - val_pre_intent_loss: 0.4133 - val_pre_ner_loss: 0.1932 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 260/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.6089 - val_pre_intent_loss: 0.4149 - val_pre_ner_loss: 0.1941 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 261/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9970 - val_loss: 0.6096 - val_pre_intent_loss: 0.4155 - val_pre_ner_loss: 0.1942 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 262/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9966 - val_loss: 0.6101 - val_pre_intent_loss: 0.4157 - val_pre_ner_loss: 0.1944 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 263/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.6123 - val_pre_intent_loss: 0.4175 - val_pre_ner_loss: 0.1948 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 264/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.6116 - val_pre_intent_loss: 0.4170 - val_pre_ner_loss: 0.1947 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 265/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6117 - val_pre_intent_loss: 0.4170 - val_pre_ner_loss: 0.1947 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 266/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6125 - val_pre_intent_loss: 0.4175 - val_pre_ner_loss: 0.1950 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 267/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.6126 - val_pre_intent_loss: 0.4178 - val_pre_ner_loss: 0.1948 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 268/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6124 - val_pre_intent_loss: 0.4178 - val_pre_ner_loss: 0.1946 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 269/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0109 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6136 - val_pre_intent_loss: 0.4183 - val_pre_ner_loss: 0.1953 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 270/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9967 - val_loss: 0.6139 - val_pre_intent_loss: 0.4186 - val_pre_ner_loss: 0.1953 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 271/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0109 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.6143 - val_pre_intent_loss: 0.4188 - val_pre_ner_loss: 0.1955 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 272/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6144 - val_pre_intent_loss: 0.4191 - val_pre_ner_loss: 0.1953 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 273/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0109 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6142 - val_pre_intent_loss: 0.4189 - val_pre_ner_loss: 0.1953 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 274/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6156 - val_pre_intent_loss: 0.4200 - val_pre_ner_loss: 0.1957 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 275/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6154 - val_pre_intent_loss: 0.4198 - val_pre_ner_loss: 0.1956 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 276/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9971 - val_loss: 0.6163 - val_pre_intent_loss: 0.4209 - val_pre_ner_loss: 0.1954 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 277/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0109 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9963 - val_loss: 0.6170 - val_pre_intent_loss: 0.4212 - val_pre_ner_loss: 0.1958 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 278/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6173 - val_pre_intent_loss: 0.4217 - val_pre_ner_loss: 0.1956 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 279/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6197 - val_pre_intent_loss: 0.4237 - val_pre_ner_loss: 0.1960 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 280/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.6193 - val_pre_intent_loss: 0.4231 - val_pre_ner_loss: 0.1962 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 281/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9967 - val_loss: 0.6195 - val_pre_intent_loss: 0.4234 - val_pre_ner_loss: 0.1961 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 282/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.6193 - val_pre_intent_loss: 0.4235 - val_pre_ner_loss: 0.1958 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 283/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9965 - val_loss: 0.6201 - val_pre_intent_loss: 0.4238 - val_pre_ner_loss: 0.1962 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 284/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6189 - val_pre_intent_loss: 0.4231 - val_pre_ner_loss: 0.1958 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 285/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.6187 - val_pre_intent_loss: 0.4227 - val_pre_ner_loss: 0.1961 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 286/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6197 - val_pre_intent_loss: 0.4234 - val_pre_ner_loss: 0.1963 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 287/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6212 - val_pre_intent_loss: 0.4251 - val_pre_ner_loss: 0.1961 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 288/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6216 - val_pre_intent_loss: 0.4256 - val_pre_ner_loss: 0.1960 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 289/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6223 - val_pre_intent_loss: 0.4263 - val_pre_ner_loss: 0.1960 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 290/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6224 - val_pre_intent_loss: 0.4265 - val_pre_ner_loss: 0.1959 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 291/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6221 - val_pre_intent_loss: 0.4261 - val_pre_ner_loss: 0.1960 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 292/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6226 - val_pre_intent_loss: 0.4265 - val_pre_ner_loss: 0.1961 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 293/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6238 - val_pre_intent_loss: 0.4271 - val_pre_ner_loss: 0.1967 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 294/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6239 - val_pre_intent_loss: 0.4276 - val_pre_ner_loss: 0.1963 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 295/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9969 - val_loss: 0.6239 - val_pre_intent_loss: 0.4281 - val_pre_ner_loss: 0.1958 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 296/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9964 - val_loss: 0.6248 - val_pre_intent_loss: 0.4283 - val_pre_ner_loss: 0.1965 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 297/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6250 - val_pre_intent_loss: 0.4285 - val_pre_ner_loss: 0.1965 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 298/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6262 - val_pre_intent_loss: 0.4297 - val_pre_ner_loss: 0.1964 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 299/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0109 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6258 - val_pre_intent_loss: 0.4290 - val_pre_ner_loss: 0.1968 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 300/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.6270 - val_pre_intent_loss: 0.4304 - val_pre_ner_loss: 0.1966 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 301/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.6264 - val_pre_intent_loss: 0.4300 - val_pre_ner_loss: 0.1964 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 302/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.6270 - val_pre_intent_loss: 0.4306 - val_pre_ner_loss: 0.1964 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 303/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.6287 - val_pre_intent_loss: 0.4317 - val_pre_ner_loss: 0.1970 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 304/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.6291 - val_pre_intent_loss: 0.4322 - val_pre_ner_loss: 0.1969 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 305/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6300 - val_pre_intent_loss: 0.4328 - val_pre_ner_loss: 0.1971 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 306/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6308 - val_pre_intent_loss: 0.4338 - val_pre_ner_loss: 0.1970 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 307/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6304 - val_pre_intent_loss: 0.4338 - val_pre_ner_loss: 0.1966 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 308/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6311 - val_pre_intent_loss: 0.4340 - val_pre_ner_loss: 0.1971 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 309/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6309 - val_pre_intent_loss: 0.4342 - val_pre_ner_loss: 0.1968 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 310/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.6309 - val_pre_intent_loss: 0.4343 - val_pre_ner_loss: 0.1966 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 311/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6316 - val_pre_intent_loss: 0.4348 - val_pre_ner_loss: 0.1968 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 312/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6322 - val_pre_intent_loss: 0.4352 - val_pre_ner_loss: 0.1970 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 313/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9964 - val_loss: 0.6321 - val_pre_intent_loss: 0.4353 - val_pre_ner_loss: 0.1969 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 314/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6328 - val_pre_intent_loss: 0.4359 - val_pre_ner_loss: 0.1968 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 315/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6320 - val_pre_intent_loss: 0.4352 - val_pre_ner_loss: 0.1968 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 316/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6343 - val_pre_intent_loss: 0.4372 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 317/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6342 - val_pre_intent_loss: 0.4369 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 318/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.6337 - val_pre_intent_loss: 0.4367 - val_pre_ner_loss: 0.1970 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 319/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6339 - val_pre_intent_loss: 0.4370 - val_pre_ner_loss: 0.1969 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 320/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9966 - val_loss: 0.6348 - val_pre_intent_loss: 0.4376 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 321/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.6351 - val_pre_intent_loss: 0.4381 - val_pre_ner_loss: 0.1970 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 322/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.6355 - val_pre_intent_loss: 0.4383 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 323/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6356 - val_pre_intent_loss: 0.4385 - val_pre_ner_loss: 0.1971 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 324/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6360 - val_pre_intent_loss: 0.4389 - val_pre_ner_loss: 0.1971 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 325/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9969 - val_loss: 0.6361 - val_pre_intent_loss: 0.4389 - val_pre_ner_loss: 0.1971 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 326/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6370 - val_pre_intent_loss: 0.4396 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 327/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6371 - val_pre_intent_loss: 0.4399 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 328/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6375 - val_pre_intent_loss: 0.4403 - val_pre_ner_loss: 0.1971 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 329/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9971 - val_loss: 0.6373 - val_pre_intent_loss: 0.4401 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 330/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9966 - val_loss: 0.6373 - val_pre_intent_loss: 0.4401 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 331/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6382 - val_pre_intent_loss: 0.4409 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 332/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6380 - val_pre_intent_loss: 0.4408 - val_pre_ner_loss: 0.1972 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 333/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.6386 - val_pre_intent_loss: 0.4412 - val_pre_ner_loss: 0.1975 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 334/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.6390 - val_pre_intent_loss: 0.4417 - val_pre_ner_loss: 0.1973 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 335/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6396 - val_pre_intent_loss: 0.4422 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 336/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9968 - val_loss: 0.6402 - val_pre_intent_loss: 0.4428 - val_pre_ner_loss: 0.1975 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 337/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6400 - val_pre_intent_loss: 0.4427 - val_pre_ner_loss: 0.1973 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 338/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6401 - val_pre_intent_loss: 0.4427 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 339/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6411 - val_pre_intent_loss: 0.4436 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 340/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.6407 - val_pre_intent_loss: 0.4434 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 341/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6421 - val_pre_intent_loss: 0.4445 - val_pre_ner_loss: 0.1976 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 342/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6419 - val_pre_intent_loss: 0.4443 - val_pre_ner_loss: 0.1976 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 343/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6425 - val_pre_intent_loss: 0.4450 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 344/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.6424 - val_pre_intent_loss: 0.4451 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 345/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6426 - val_pre_intent_loss: 0.4451 - val_pre_ner_loss: 0.1975 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 346/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6423 - val_pre_intent_loss: 0.4449 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 347/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.6435 - val_pre_intent_loss: 0.4458 - val_pre_ner_loss: 0.1977 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 348/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.6427 - val_pre_intent_loss: 0.4453 - val_pre_ner_loss: 0.1974 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 349/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9967 - val_loss: 0.6437 - val_pre_intent_loss: 0.4460 - val_pre_ner_loss: 0.1977 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 350/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.6442 - val_pre_intent_loss: 0.4464 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 351/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9969 - val_loss: 0.6446 - val_pre_intent_loss: 0.4469 - val_pre_ner_loss: 0.1977 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 352/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6451 - val_pre_intent_loss: 0.4474 - val_pre_ner_loss: 0.1977 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 353/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6448 - val_pre_intent_loss: 0.4473 - val_pre_ner_loss: 0.1976 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 354/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9966 - val_loss: 0.6460 - val_pre_intent_loss: 0.4481 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 355/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.6457 - val_pre_intent_loss: 0.4479 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 356/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6464 - val_pre_intent_loss: 0.4484 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 357/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.6470 - val_pre_intent_loss: 0.4491 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 358/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.6477 - val_pre_intent_loss: 0.4497 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 359/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.6470 - val_pre_intent_loss: 0.4492 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9743\n",
      "Epoch 360/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6470 - val_pre_intent_loss: 0.4492 - val_pre_ner_loss: 0.1977 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 361/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6476 - val_pre_intent_loss: 0.4497 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 362/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6474 - val_pre_intent_loss: 0.4497 - val_pre_ner_loss: 0.1977 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 363/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6479 - val_pre_intent_loss: 0.4500 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 364/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6478 - val_pre_intent_loss: 0.4500 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 365/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6482 - val_pre_intent_loss: 0.4504 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 366/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6484 - val_pre_intent_loss: 0.4505 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 367/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6486 - val_pre_intent_loss: 0.4508 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 368/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6489 - val_pre_intent_loss: 0.4509 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 369/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6491 - val_pre_intent_loss: 0.4511 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 370/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6493 - val_pre_intent_loss: 0.4514 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 371/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6494 - val_pre_intent_loss: 0.4514 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 372/500\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0113 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.996 - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.6494 - val_pre_intent_loss: 0.4515 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 373/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6499 - val_pre_intent_loss: 0.4519 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 374/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6500 - val_pre_intent_loss: 0.4519 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 375/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6502 - val_pre_intent_loss: 0.4522 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 376/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6508 - val_pre_intent_loss: 0.4526 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 377/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.6506 - val_pre_intent_loss: 0.4526 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 378/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.6509 - val_pre_intent_loss: 0.4528 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 379/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.6511 - val_pre_intent_loss: 0.4530 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 380/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6512 - val_pre_intent_loss: 0.4532 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 381/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6514 - val_pre_intent_loss: 0.4533 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 382/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6515 - val_pre_intent_loss: 0.4536 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 383/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6516 - val_pre_intent_loss: 0.4537 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 384/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6521 - val_pre_intent_loss: 0.4540 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 385/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.6525 - val_pre_intent_loss: 0.4543 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 386/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.6524 - val_pre_intent_loss: 0.4543 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 387/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9967 - val_loss: 0.6524 - val_pre_intent_loss: 0.4544 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 388/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6526 - val_pre_intent_loss: 0.4546 - val_pre_ner_loss: 0.1980 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9743\n",
      "Epoch 389/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6530 - val_pre_intent_loss: 0.4548 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 390/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.6535 - val_pre_intent_loss: 0.4552 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 391/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.6536 - val_pre_intent_loss: 0.4553 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 392/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6535 - val_pre_intent_loss: 0.4553 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 393/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6539 - val_pre_intent_loss: 0.4556 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 394/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6538 - val_pre_intent_loss: 0.4556 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 395/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9964 - val_loss: 0.6539 - val_pre_intent_loss: 0.4558 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 396/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6542 - val_pre_intent_loss: 0.4561 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 397/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6543 - val_pre_intent_loss: 0.4561 - val_pre_ner_loss: 0.1981 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 398/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6546 - val_pre_intent_loss: 0.4564 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 399/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6547 - val_pre_intent_loss: 0.4565 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 400/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.6546 - val_pre_intent_loss: 0.4564 - val_pre_ner_loss: 0.1982 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 401/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9969 - val_loss: 0.6551 - val_pre_intent_loss: 0.4568 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 402/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.6551 - val_pre_intent_loss: 0.4568 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 403/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9964 - val_loss: 0.6556 - val_pre_intent_loss: 0.4572 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 404/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.6556 - val_pre_intent_loss: 0.4573 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 405/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9970 - val_loss: 0.6560 - val_pre_intent_loss: 0.4574 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 406/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9967 - val_loss: 0.6562 - val_pre_intent_loss: 0.4578 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 407/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.6563 - val_pre_intent_loss: 0.4580 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 408/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6562 - val_pre_intent_loss: 0.4579 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 409/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.6563 - val_pre_intent_loss: 0.4580 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 410/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.6564 - val_pre_intent_loss: 0.4580 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 411/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.6566 - val_pre_intent_loss: 0.4583 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 412/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6568 - val_pre_intent_loss: 0.4584 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 413/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6569 - val_pre_intent_loss: 0.4585 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 414/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6569 - val_pre_intent_loss: 0.4585 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 415/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6570 - val_pre_intent_loss: 0.4587 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 416/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6572 - val_pre_intent_loss: 0.4588 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 417/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6572 - val_pre_intent_loss: 0.4589 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 418/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.6574 - val_pre_intent_loss: 0.4590 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 419/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6574 - val_pre_intent_loss: 0.4590 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 420/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6576 - val_pre_intent_loss: 0.4592 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 421/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6575 - val_pre_intent_loss: 0.4591 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 422/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6577 - val_pre_intent_loss: 0.4592 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 423/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.6578 - val_pre_intent_loss: 0.4594 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 424/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.6579 - val_pre_intent_loss: 0.4595 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 425/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.6581 - val_pre_intent_loss: 0.4597 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 426/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6583 - val_pre_intent_loss: 0.4598 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 427/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6581 - val_pre_intent_loss: 0.4597 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 428/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9970 - val_loss: 0.6584 - val_pre_intent_loss: 0.4599 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 429/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6585 - val_pre_intent_loss: 0.4600 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 430/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.6585 - val_pre_intent_loss: 0.4601 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 431/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6586 - val_pre_intent_loss: 0.4602 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 432/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6585 - val_pre_intent_loss: 0.4601 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 433/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6589 - val_pre_intent_loss: 0.4604 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 434/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6591 - val_pre_intent_loss: 0.4606 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 435/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.6590 - val_pre_intent_loss: 0.4605 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 436/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9965 - val_loss: 0.6591 - val_pre_intent_loss: 0.4606 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 437/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.6593 - val_pre_intent_loss: 0.4608 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 438/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.6593 - val_pre_intent_loss: 0.4608 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 439/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6593 - val_pre_intent_loss: 0.4608 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 440/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6595 - val_pre_intent_loss: 0.4610 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 441/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6596 - val_pre_intent_loss: 0.4610 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 442/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.6599 - val_pre_intent_loss: 0.4613 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 443/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6598 - val_pre_intent_loss: 0.4613 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 444/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6600 - val_pre_intent_loss: 0.4614 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 445/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9971 - val_loss: 0.6599 - val_pre_intent_loss: 0.4614 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 446/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6601 - val_pre_intent_loss: 0.4615 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 447/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6601 - val_pre_intent_loss: 0.4616 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 448/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6603 - val_pre_intent_loss: 0.4617 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 449/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.6603 - val_pre_intent_loss: 0.4618 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 450/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9970 - val_loss: 0.6606 - val_pre_intent_loss: 0.4620 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 451/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.6607 - val_pre_intent_loss: 0.4621 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 452/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6606 - val_pre_intent_loss: 0.4620 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 453/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.6608 - val_pre_intent_loss: 0.4622 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 454/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.6608 - val_pre_intent_loss: 0.4622 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 455/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.6609 - val_pre_intent_loss: 0.4623 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 456/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6610 - val_pre_intent_loss: 0.4624 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 457/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.6611 - val_pre_intent_loss: 0.4625 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 458/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6612 - val_pre_intent_loss: 0.4626 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9741\n",
      "Epoch 459/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6613 - val_pre_intent_loss: 0.4626 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 460/500\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0104 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9941 - pre_ner_accuracy: 0.997 - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.6615 - val_pre_intent_loss: 0.4628 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 461/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6614 - val_pre_intent_loss: 0.4627 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 462/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9972 - val_loss: 0.6615 - val_pre_intent_loss: 0.4628 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 463/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6616 - val_pre_intent_loss: 0.4629 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 464/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6615 - val_pre_intent_loss: 0.4629 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 465/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6616 - val_pre_intent_loss: 0.4629 - val_pre_ner_loss: 0.1986 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 466/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.6616 - val_pre_intent_loss: 0.4630 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 467/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.6617 - val_pre_intent_loss: 0.4631 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 468/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6618 - val_pre_intent_loss: 0.4631 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 469/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6619 - val_pre_intent_loss: 0.4632 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 470/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6619 - val_pre_intent_loss: 0.4632 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 471/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6618 - val_pre_intent_loss: 0.4632 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 472/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.6620 - val_pre_intent_loss: 0.4633 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 473/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.6620 - val_pre_intent_loss: 0.4633 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 474/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6620 - val_pre_intent_loss: 0.4634 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 475/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9955 - pre_ner_accuracy: 0.9971 - val_loss: 0.6621 - val_pre_intent_loss: 0.4634 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 476/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6622 - val_pre_intent_loss: 0.4635 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 477/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.6623 - val_pre_intent_loss: 0.4635 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 478/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6623 - val_pre_intent_loss: 0.4636 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 479/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6623 - val_pre_intent_loss: 0.4636 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 480/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6624 - val_pre_intent_loss: 0.4637 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 481/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6625 - val_pre_intent_loss: 0.4638 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 482/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9972 - val_loss: 0.6625 - val_pre_intent_loss: 0.4638 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 483/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6625 - val_pre_intent_loss: 0.4638 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 484/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6625 - val_pre_intent_loss: 0.4638 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 485/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6626 - val_pre_intent_loss: 0.4639 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 486/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6626 - val_pre_intent_loss: 0.4639 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 487/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.6628 - val_pre_intent_loss: 0.4640 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 488/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6628 - val_pre_intent_loss: 0.4641 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 489/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.6628 - val_pre_intent_loss: 0.4641 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 490/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6629 - val_pre_intent_loss: 0.4642 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 491/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6629 - val_pre_intent_loss: 0.4642 - val_pre_ner_loss: 0.1988 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 492/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.6630 - val_pre_intent_loss: 0.4643 - val_pre_ner_loss: 0.1988 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 493/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6630 - val_pre_intent_loss: 0.4642 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 494/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6631 - val_pre_intent_loss: 0.4643 - val_pre_ner_loss: 0.1988 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 495/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.6631 - val_pre_intent_loss: 0.4644 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 496/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.6631 - val_pre_intent_loss: 0.4644 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 497/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.6631 - val_pre_intent_loss: 0.4644 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 498/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.6633 - val_pre_intent_loss: 0.4645 - val_pre_ner_loss: 0.1988 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 499/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.6633 - val_pre_intent_loss: 0.4646 - val_pre_ner_loss: 0.1988 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 500/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.6633 - val_pre_intent_loss: 0.4645 - val_pre_ner_loss: 0.1988 - val_pre_intent_accuracy: 0.9286 - val_pre_ner_accuracy: 0.9739\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f8a2c2fa5d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_dataset,epochs=params['epochs'],validation_data=valid_dataset,callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model.fit(tarin_seq, [train_intent, train_ner],epochs=params['epochs'],validation_split=0.1,callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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